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IDENTIFIYING GROUPS WHO DEAL IN OVER-THE-COUNTER DERIVATIVES: HOW SPECULATION LEADS TO FINANCIAL TURMOIL

Description

Since 1994, the Performance Based Studies Research Group at Arizona State University has utilized an approach to industry called Best Value (BV). Since its origin, this approach has been used in 1860 tests creating $6.4 billion dollars of projects and services delivered, at a customer satisfaction rating of 95%. Best…

Since 1994, the Performance Based Studies Research Group at Arizona State University has utilized an approach to industry called Best Value (BV). Since its origin, this approach has been used in 1860 tests creating $6.4 billion dollars of projects and services delivered, at a customer satisfaction rating of 95%. Best Value (BV) is rooted in simplicity, and seeks to help organizations hire experts, plan ahead, minimize risk, optimize resources, and optimize resources. This is accomplished largely through the use of a tool the PBSRG calls the Kashiwagi Solution Model (KSM). Kashiwagi Solution Models can be used across every industry from construction to Wall Street to help achieve sustainable success in what is perhaps the most efficient and effective manner available today. Using Best Value (BV) and the Kashiwagi Solution Model (KSM), the author identified groups on Wall Street and throughout the world who deal in a unique entity called "Over-The-Counter (OTC) Derivatives". More specifically, this paper focuses on the current status and ramifications of derivative contracts that two parties enter with the sole intention of speculating. KSMs are used in Information Measurement Theory, which seeks to take seemingly complex subjects and simplify them into terms that everyone can understand. This document uses Information Measurement Theory to explain what OTC derivatives are in the simplest possible way, so that little prior knowledge of finance is required to understand the material. Through research and observation, KSMs can be used to identify the characteristics of groups who deal in OTC derivatives, which contributed to the financial crisis in 2008 and have grown in size and complexity. This document uses dominant information in order to see the potential problems within the OTC derivatives market from 30,000 feet, and offer solutions to those problems. Keywords: simplicity, best value approach, identify characteristics, dominant information

ContributorsBills, Andrew Marius (Author) / Kashiwagi, Dean (Thesis director) / Kashiwagi, Jacob (Committee member) / Rivera, Alfredo (Committee member) / Department of Finance (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)

Created2016-05

Probabilistic Modeling and Regression Analysis of Experimental Data for Structural Systems

Description

The Experimental Data Processing (EDP) software is a C++ GUI-based application to streamline the process of creating a model for structural systems based on experimental data. EDP is designed to process raw data, filter the data for noise and outliers, create a fitted model to describe that data, complete a…

The Experimental Data Processing (EDP) software is a C++ GUI-based application to streamline the process of creating a model for structural systems based on experimental data. EDP is designed to process raw data, filter the data for noise and outliers, create a fitted model to describe that data, complete a probabilistic analysis to describe the variation between replicates of the experimental process, and analyze reliability of a structural system based on that model. In order to help design the EDP software to perform the full analysis, the probabilistic and regression modeling aspects of this analysis have been explored. The focus has been on creating and analyzing probabilistic models for the data, adding multivariate and nonparametric fits to raw data, and developing computational techniques that allow for these methods to be properly implemented within EDP. For creating a probabilistic model of replicate data, the normal, lognormal, gamma, Weibull, and generalized exponential distributions have been explored. Goodness-of-fit tests, including the chi-squared, Anderson-Darling, and Kolmogorov-Smirnoff tests, have been used in order to analyze the effectiveness of any of these probabilistic models in describing the variation of parameters between replicates of an experimental test. An example using Young's modulus data for a Kevlar-49 Swath stress-strain test was used in order to demonstrate how this analysis is performed within EDP. In order to implement the distributions, numerical solutions for the gamma, beta, and hypergeometric functions were implemented, along with an arbitrary precision library to store numbers that exceed the maximum size of double-precision floating point digits. To create a multivariate fit, the multilinear solution was created as the simplest solution to the multivariate regression problem. This solution was then extended to solve nonlinear problems that can be linearized into multiple separable terms. These problems were solved analytically with the closed-form solution for the multilinear regression, and then by using a QR decomposition to solve numerically while avoiding numerical instabilities associated with matrix inversion. For nonparametric regression, or smoothing, the loess method was developed as a robust technique for filtering noise while maintaining the general structure of the data points. The loess solution was created by addressing concerns associated with simpler smoothing methods, including the running mean, running line, and kernel smoothing techniques, and combining the ability of each of these methods to resolve those issues. The loess smoothing method involves weighting each point in a partition of the data set, and then adding either a line or a polynomial fit within that partition. Both linear and quadratic methods were applied to a carbon fiber compression test, showing that the quadratic model was more accurate but the linear model had a shape that was more effective for analyzing the experimental data. Finally, the EDP program itself was explored to consider its current functionalities for processing data, as described by shear tests on carbon fiber data, and the future functionalities to be developed. The probabilistic and raw data processing capabilities were demonstrated within EDP, and the multivariate and loess analysis was demonstrated using R. As the functionality and relevant considerations for these methods have been developed, the immediate goal is to finish implementing and integrating these additional features into a version of EDP that performs a full streamlined structural analysis on experimental data.

ContributorsMarkov, Elan Richard (Author) / Rajan, Subramaniam (Thesis director) / Khaled, Bilal (Committee member) / Chemical Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Ira A. Fulton School of Engineering (Contributor) / Barrett, The Honors College (Contributor)

Created2016-05

Air Quality, Community Engagement, and Emerging Technologies

Description

Maricopa County is the home of the Phoenix metropolitan area, an expansive city with serious air quality concerns. To ameliorate air quality in the county, the Maricopa County Air Quality Department developed a website and mobile application called "Clean Air Make More" as a means of outreach and engagement. In…

Maricopa County is the home of the Phoenix metropolitan area, an expansive city with serious air quality concerns. To ameliorate air quality in the county, the Maricopa County Air Quality Department developed a website and mobile application called "Clean Air Make More" as a means of outreach and engagement. In doing this, the county has found a way to engender a bilateral relationship between individuals and their government agency. This study analyzes the effectiveness of Clean Air Make More in establishing this relationship and engaging the community in efforts to improve air quality. It concludes that the design of the application effectively meets user needs, but marketing efforts should target populations disposed to taking action regarding air quality.

ContributorsLapoint, Maggie Lane (Author) / Johnston, Erik W., 1977- (Thesis director) / Hondula, David M. (Committee member) / Barrett, The Honors College (Contributor) / W. P. Carey School of Business (Contributor) / School of International Letters and Cultures (Contributor)

Created2015-05

Opportunities and Challenges for Personal Heat Exposure Research

Description

Background:
Environmental heat exposure is a public health concern. The impacts of environmental heat on mortality and morbidity at the population scale are well documented, but little is known about specific exposures that individuals experience.

Objectives:
The first objective of this work was to catalyze discussion of the role of personal heat exposure…

Background:
Environmental heat exposure is a public health concern. The impacts of environmental heat on mortality and morbidity at the population scale are well documented, but little is known about specific exposures that individuals experience.

Objectives:
The first objective of this work was to catalyze discussion of the role of personal heat exposure information in research and risk assessment. The second objective was to provide guidance regarding the operationalization of personal heat exposure research methods.

Discussion:
We define personal heat exposure as realized contact between a person and an indoor or outdoor environment that poses a risk of increases in body core temperature and/or perceived discomfort. Personal heat exposure can be measured directly with wearable monitors or estimated indirectly through the combination of time–activity and meteorological data sets. Complementary information to understand individual-scale drivers of behavior, susceptibility, and health and comfort outcomes can be collected from additional monitors, surveys, interviews, ethnographic approaches, and additional social and health data sets. Personal exposure research can help reveal the extent of exposure misclassification that occurs when individual exposure to heat is estimated using ambient temperature measured at fixed sites and can provide insights for epidemiological risk assessment concerning extreme heat.

Conclusions:
Personal heat exposure research provides more valid and precise insights into how often people encounter heat conditions and when, where, to whom, and why these encounters occur. Published literature on personal heat exposure is limited to date, but existing studies point to opportunities to inform public health practice regarding extreme heat, particularly where fine-scale precision is needed to reduce health consequences of heat exposure.

ContributorsKuras, Evan R. (Author) / Richardson, Molly B. (Author) / Calkins, Mirian M. (Author) / Ebi, Kristie L. (Author) / Gohlke, Julia M. (Author) / Hess, Jeremy J. (Author) / Hondula, David M. (Author) / Kintziger, Kristina W. (Author) / Jagger, Meredith A. (Author) / Middel, Ariane (Author) / Scott, Anna A. (Author) / Spector, June T. (Contributor) / Uejio, Christopher K. (Author) / Vanos, Jennifer K. (Author) / Zaitchik, Benjamin F. (Author)

Created2017-08

Arizona Extreme Weather and Public Health Workshop Summary Report

Description

In June 2016, the Arizona Department of Health Services (ADHS) with researchers from Arizona State University (ASU) convened a one-day workshop of public health professionals and experts from Arizona’s county and state agencies to advance statewide preparedness for extreme weather events and climate change. The United States Centers for Disease…

In June 2016, the Arizona Department of Health Services (ADHS) with researchers from Arizona State University (ASU) convened a one-day workshop of public health professionals and experts from Arizona’s county and state agencies to advance statewide preparedness for extreme weather events and climate change. The United States Centers for Disease Control and Prevention (CDC) sponsors the Climate-Ready Cities and States Initiative, which aims to help communities across the country prepare for and prevent projected disease burden associated with climate change. Arizona is one of 18 public health jurisdictions funded under this initiative. ADHS is deploying the CDC’s five-step Building Resilience Against Climate Effects (BRACE) framework to assist counties and local public health partners with becoming better prepared to face challenges associated with the impacts of climate-sensitive hazards. Workshop participants engaged in facilitated exercises designed to rigorously consider social vulnerability to hazards in Arizona and to prioritize intervention activities for extreme heat, wildfire, air pollution, and flooding.

This report summarizes the proceedings of the workshop focusing primarily on two sessions: the first related to social vulnerability mapping and the second related to the identification and prioritization of interventions necessary to address the impacts of climate-sensitive hazards.

ContributorsRoach, Matthew (Author) / Hondula, David M. (Author) / Putnam, Hana (Author) / Chhetri, Nalini (Author) / Chakalian, Paul (Author) / Watkins, Lance (Author) / Dufour, Brigette (Author)

Created2016-11-28